For this weeks Makeover Monday we are taking a look at consumer spending by generation and category. In the original visualization below you can easily see how each individual generation spent in each category.
I thought it would be interesting to break the data visualization out by category. I wanted to easily scan the categories to see how each generation spent differently.
By breaking the visualization out by category I found it easier to see what categories each generation valued the most. For instance, you instantly see that Traditionalist are more likely to cook at home as they spend the most on Groceries and the least in Resturants. You can also see how the Millenials are the complete opposite of the Traditionalists in these categories with the lowest spending in Grocery complemented by the highest spending in Resturants. As a Millennial who rarely eats out and cooks most meals from scratch this one initially surprised me.
You can also see other interesting associations, like how Millennials spend the most on Hobbies, and the least on Furniture/Building. As a generation that values experiences over things, these spending patterns make sense.
After looking at both visualizations what questions do you have? Did you create a Makeover Monday visualization? Share the link, I would love to see it.
This week for Make Over Monday we are looking at data for World Development Indicators – Health and Equality. There was several different aspects of health and equality that we could have looked at with the data provided. I decided to focus on the Average Female Life expectancy world wide from 1960 to 2016.
I thought it would be interesting to see the trend and how this number has changed for different countries around the world.
For this first visualization I thought it would be interesting to explore the over all world trend. The over all trend is that the life expectancy for women has steadily increased. While this may not come as a surprise it was interesting to see that several countries have seen wild swings with lower life expectancy then rebounding.
When inspecting which countries experienced sever fluctuations we can see that these are countries that have had serious conflicts in recent history such as Rwanda and Sierra Leon. This would explain these rapid fluctuations shown in this visualization.
Now that we have an idea of how female life expectancy has been trending I thought it would be useful to see how different countries compare with one another in the most recent recorded year. This world map shows female life expectancy for 2016. We can see that developed countries have a longer life expectancy than developing nations. There is a wide gap between the countries with the highest life expectancy which is well into the 80’s and the countries with the lowest life expectancy, which are coming in at the low 50’s.
Finally I thought it would be interesting to be able to see visually how each countries life expectancy has changed over the years. While most countries follow the trend that we saw in the first visualization we can also see those countries that had dips in their life expectancy and how those numbers rose and fell over the years.
With the data this week I felt that it was important to look for over all trends as well individual variances. While we are becoming an increasingly integrated world economy the data shows that there is also large gaps between developing and developed nations.
This week for Make Over Monday we are looking at dataset for the economic value of the bicycle industry in the UK. Cycling is one of my favorite hobbies so I was excited to jump in and make some fun visualizations.
Because I love bicycles, and I thought they would make for great visualizations, I wanted to use custom shapes for my graphs this week. I imported a custom icon of a bicycle to be used across my visualizations.
For the first visualization I wanted to take a look at the number of manual bicycles that were imported each year. I added a dual axis with the same data to create this bicycle carousel effect. I felt that adding the bar graph element made it easier to see the change in trends and the significant decline that the bicycle industry experienced in 2016.
Next, I thought it would be interesting to see how manual bicycle imports compared with electric bicycle imports. This graphic shows that while electric bikes may have carved out their own portion of the market, manual bikes still make up the majority of the imports.
Finally, I thought that it was important to show electric bikes in their own visual which shows a sharp climb in imports in 2015
With a sharp rise in 2015 that then fell significantly in 2016 I was curious what could have caused this fluctuation in the market. For answers I went to my local bike shop pro. He explained to me a little of the history behind the e-bike and how it continued to develop over the years. He explained that this rise was likely due to the development of a better battery and ingratiation of that battery into the frame. E-bikes saw a rise in popularity after these improvements. He also had a possible explanation for the decline. He explained that in his experience E-bikes were a one time purchase, he did not see customers coming in the next year to upgrade to a new model. If a large population of riders interested in E-bikes bought theirs the year that the improved design came out it could explain how the number of E-bikes needed would then decline the next year.
I had a lot of fun this week learning some new skills in Tableau I focused on using custom shapes and dual axis in all of my visualizations and love the different stories in the data I was able to show using these techniques.
Did you participate in Make Over Monday? I’d love it if you would share a link to your visualization and share any useful insights you learned.
For Make Over Monday this week we are looking at data for wind energy produced by each state in 2018. The original visulisation was a bar graph similar to the one below.
I wanted to include a similar bar graph in my visualizations because the bar graph did a great job of illustrating the large range of equivalent house holds powered by wind energy. It clearly shows how large the difference is between the state that powers the most households with wind energy, Texas, even when compared with the next largest producer of wind energy Oklahoma. This visualization clearly shows that Texas is leading the way when it comes to powering homes with wind energy.
I also thought it would be useful to see this data displayed in a geographical context which is why I created this visualization of the continental united states.
The color gradient in this visualization also highlights Texas as the largest producer of wind energy in the United States. You can also see that there appears to be more wind energy produced in the midwest, followed by the west coast when compared to the east coast indicating regional differences. These differences by region could lead to further investigations, are these differences due to climate, space, or politics?
Finally I wanted a table of rankings for quick and easy reference which you can find below.
Did you create a Tableau visualization for Make Over Monday? I would love to see it, leave your link in the comments below.
I am just getting started with visualizations in Tableau and what better way to learn than to jump in with Make Over Monday. I am new to the software and still learning, so rather than try and make a high tech dashboard or complicated graphic I decided to work with the skills I have and instead focus on answering a question. I decided to go with this approach because the reality is a data visualization should answer a questions, otherwise it is just a pretty graphic.
After looking at the original graphic, which you can see here, I was left wondering what the energy use trends looked like. Was there a day of the week or time of day where energy use was significantly higher or lower? I felt that looking at this trend would tell us more about how the energy is used at Downing Street than simply showing a running log of watts used and carbon impact. With this in mind I created the following visualization.
Given that the Downing Street complex is both a home and a busy office it makes sense that power usage would be higher during the week when office staff would also be in the townhouse working. The energy usage data shows that is during business hours when energy usage is highest. If the goal where to reduce the carbon impact this would suggest that green solutions for the office area would have the highest impact.
After looking at the original graphic what question would you like answered? If you created a Make Over Monday Visualization of your own share a link in the comments, I would love to see what you did with the data. Until next next time, happy analyzing.
Did “learn to code” make your list of goals this year? If it did you may be wondering where to start? If making a career change into tech also made that list then you know already know the wide range of jobs, qualifications, and experience needed. As you peruse the job listings you may find that the required qualifications look more like a list of nonsense words you have never heard of:
C+ Java Python Ruby PHP
SQL Pig Hive MongoDB
Hadoop Apache Spark
The list of jumbled letters and strange words could go on for pages, you may even wonder are these actual qualifications or is someone playing a crule joke?
It was around this time last year that I knew I wanted to transition my career into the tech space. Believe me I know, taking a glance at the job qualifications can be downright disheartening. Those job postings are full of qualifications that you have never even heard of, let alone have!
Now take a deep breath, because it gets better. You don’t have to know all of the computer programing languages, libraries, or technologies to get your first job. You will need to know a few relevant technologies for your field, the importnat thing now is to figure out where to focus your efforts.
There is no exact science to it, the most important thing is that you get started. I am going to share with you the exact steps I took to decided what language to learn that helped me get my first tech job.
1. Pick Your Path
Decided what it is you would like to do. The minimum skills need to be a front end web developer are far different than those needed to be a data analyst. Decided what it is you would like to do in tech then focus your efforts there.
Not sure what you would like to do? Don’t let that stop you from getting started, you can always learn something new if you find a different field you are passionate about along the way. The most important thing right now is to start. Don’t let the fear of learning the “wrong thing” keep you from getting started.
2. Make A Master List
Have you selected the type of tech job you would like to go after? Great! The next step is to look up job postings in your area. Start browsing your local job boards to see what companies in your area are looking for. Start a master list of the qualifications listed in each post. While it may look overwhelming at first by putting the qualifications for each of those jobs in one place you will start to see where they overlap.
In my own search, I found that almost all of the job postings I was interested in were looking for someone who could program in Python. In your own list, you will begin to see a pattern as well. Once you get a good idea of which languages are required for your field you will know where to focus your studies first.
3. Find A Course
Find a course. We are privileged to live in a time when anyone with a computer and an internet connection has access to limitless amounts of information. You can learn anything online from how to change out your bathroom faucet (speaking from experience) to machine learning algorithms! There are tons of free resources as well as paid courses, it is all about finding what will work best for you.
Personally, I first tried putting together my own course from free YouTube videos, and while I gained some basic understanding of coding and could write some simple scripts I found it time-consuming to try and find the right videos to watch next. Ultimately I felt I was wasting precious time trying to find quality content and create my own ciriculum.
I also tried TreeHouse which I think was great for a first time program. The Python course does a great job of breaking down concepts into very small, easily digestible pieces of information. But, after about a month I felt like I had no understanding of how to use the code outside of the TreeHouse platform. It was a great way to get started, and if you feel intimidated by the idea of coding I would say it is the perfect place to get your feet wet. but personally I felt like I needed a course that would show me real world applications. Which lead me to my favorite platform for learning to code!
Whatever platform you choose to learn code the final step is to jump in! When learning something completely new we have a tendency to hesitate. If you have no background in programming even the beginning lessons may feel like you were thrown in the deep end. I’ve been there! But the best thing you can do is trust that you will figure it out along the way. It is ok not to understand how it all comes together yet, keep gathering pieces of the puzzle until you start to see the big picture.
Making the big leap and changing careers can be scary, but you will be amazed at how quickly you can learn. After just a couple of months of self study I was able to get a job as a digital analyst. Was I the best programmer? Nope. But the company was impressed with my drive, they loved that I was learning Python on my own and that I was data driven. I may not have had all the skills they were looking for but I had shown a willingness to learn which gave them the confidence that they could teach me what I needed to know on the job.
There is so much you don’t know when changing careers, but if you take that leap of faith you will learn what you need to along the way. Are you looking at changing careers? Tell me in the comments below what are you doing right now to make it happen?